Bootstrapped training of event extraction classifiers
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© 2012 Association for Computational Linguistics. Most event extraction systems are trained with supervised learning and rely on a collection of annotated documents. Due to the domain-specificity of this task, event extraction systems must be retrained with new annotated data for each domain. In this paper, we propose a bootstrapping solution for event role filler extraction that requiresminimal human supervision. We aim to rapidly train a state-of-The-Art event extraction system using a small set of "seed nouns" for each event role, a collection of relevant (in-domain) and irrelevant (outof- domain) texts, and a semantic dictionary. The experimental results show that the bootstrapped system outperforms previous weakly supervised event extraction systems on the MUC-4 data set, and achieves performance levels comparable to supervised training with 700 manually annotated documents.
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